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Nutrient recovery from urine: Urea adsorption onto biochar integrated with Na-chabazite as urease inhibitor

2024· article· en· W4403349900 on OpenAlexafffund
Haotian Wu, Xavier Foster, Hossein Kazemian, Igor Cauphy Peggy Diby, Serge Kaliaguine, Céline Vaneeckhaute

Bibliographic record

VenueResources Conservation and Recycling · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Northern British ColumbiaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsChabaziteBiocharUreaseAdsorptionUreaChemistryNutrientUrineEnvironmental chemistryWaste managementBiochemistryOrganic chemistryZeolitePyrolysisCatalysis

Abstract

fetched live from OpenAlex

This study presents an innovative technical integration for concomitant nutrient recovery from source-separated urine. While cation exchange is known for efficient K + recovery, it faces competition due to the high molarity of NH 4 + in hydrolyzed urine. This study proposes inhibiting urease activity to facilitate the recovery of K⁺ and urea from fresh urine. Na-chabazite was first proposed as a urease inhibitor in this study, reducing urease activity by 50 %. Wood biochar, with its high porosity (308.0 m²/g) and polar functional groups, shows a urea adsorption capacity of 25.4 mg/g, which can be further improved by steam activation. The isotherm analysis suggests that urea adsorption onto biochar follows a multi-layer adsorption process. Finally, an integrated process is suggested: "Na-chabazite and Biochar adsorption → urea hydrolysis → struvite precipitation + ammonia stripping-acid scrubbing", ensuring efficient recovery of urea, NH 4 + , PO 4 3- , and K + from source-separated urine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.202
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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